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| ID | Type | Description | Link |
|---|---|---|---|
| R01DA044985-04 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute on Drug Abuse (NIDA) | NIH |
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The goal of this cluster randomized clinical trial is to test a clinician-targeted behavioral nudge intervention in the Electronic Health Record (EHR) for patients who are identified by a machine-learning based risk prediction model as having an elevated risk for an opioid overdose.
The clinical trial will evaluate the effectiveness of providing a flag in the EHR to identify individuals at elevated risk with and without behavioral nudges/best practice alerts (BPAs) as compared to usual care by primary care clinicians.
The primary goals of the study are to improve opioid prescribing safety and reduce overdose risk.
In response to the opioid overdose crisis, health systems have instituted multiple interventions to reduce patient risk, including decreasing unsafe opioid prescribing among high-risk patients and dispensing naloxone. However, these interventions face two key challenges. First, there are limited and poorly performing tools to identify who is truly at risk of overdose, leading to burdensome interventions targeting an overly broad population or missing key high-risk individuals. Second, even with more accurate identification of high-risk patients, highly effective strategies to change clinician behavior remain limited. Common cognitive biases may underlie clinicians' lack of response to risk factors for overdose.
This project aims to address both of these limitations by combining more accurate risk prediction tools to identify those at elevated risk of opioid overdose with novel "nudge" interventions based on principles of behavioral economics that have been shown to address cognitive biases and change prescribing behavior. The primary hypothesis is that high-risk patients in primary care practices randomized to the elevated-risk flag + nudge intervention will have safer prescribing compared to usual care.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Usual Care | Active Comparator | Patients in the practices randomized to the Usual Care arm will receive standard care practice without change. |
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| EHR-Embedded Elevated-Risk Flag | Experimental | An elevated-risk flag will be embedded in the EHR and prominently displayed in the chart during encounters for patients identified as having elevated-risk for opioid overdose. |
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| EHR-Embedded Elevated-Risk Flag with Behavioral Nudges | Experimental | An elevated-risk flag will be embedded in the EHR and prominently displayed in the chart during encounters for patients identified as having elevated-risk for opioid overdose. This flag will be combined with a set of best practice alerts/behavioral nudges that will trigger when certain conditions are met during encounters with elevated-risk patients. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| EHR-Embedded Elevated-Risk Flag | Behavioral | Clinicians seeing patients at elevated predicted risk will see a flag on the EHR 'storyboard' during in person or telephone encounters indicating the patient is at elevated predicted risk of opioid overdose. The clinician will have the option of including this information into their decision-making process when providing care. There will be no best practice alerts/behavioral nudges in this arm. |
| Measure | Description | Time Frame |
|---|---|---|
| Prescribing Practices Composite Score | This outcome measures a composite of three prescribing practices associated with a reduced risk of opioid overdose. Each of the three is assigned a value of one point, with the composite score ranging from 0 to 3:
The composite score will be treated as a 3-point ordinal measure reflecting adherence to these prescribing practices. | Assessed at 4 months following study enrollment (i.e., at 4 months after the first encounter in the study period. An encounter refers to the 1st primary care visit for a patient enrolled in the study.) |
| Measure | Description | Time Frame |
|---|---|---|
| Prescribing Practices Composite Score--6 Month Measure | This outcome measures a composite of three prescribing practices associated with a reduced risk of opioid overdose. Each of the three is assigned a value of one point, with the composite score ranging from 0 to 3: 1. Naloxone prescription: Evidence of a prescription for naloxone. 2. Opioid dosage < 50 morphine milligram equivalents (MME) per day: No prescriptions exceeding 50 MME/day during the measurement period. 3. No opioid and benzodiazepine overlap: No concurrent prescriptions for opioids and benzodiazepines. The composite score will be treated as a 3-point ordinal measure reflecting adherence to these prescribing practices. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Lead Research Program Coordinator, CP3 | Contact | (412) 692-4889 | cp3@pitt.edu |
| Name | Affiliation | Role |
|---|---|---|
| Walid F Gellad, MD, MPH | University of Pittsburgh Center for Pharmaceutical Policy and Prescribing | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Pittsburgh | Recruiting | Pittsburgh | Pennsylvania | 15213 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30901048 | Background | Lo-Ciganic WH, Huang JL, Zhang HH, Weiss JC, Wu Y, Kwoh CK, Donohue JM, Cochran G, Gordon AJ, Malone DC, Kuza CC, Gellad WF. Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions. JAMA Netw Open. 2019 Mar 1;2(3):e190968. doi: 10.1001/jamanetworkopen.2019.0968. | |
| 32678860 | Background | Lo-Ciganic WH, Huang JL, Zhang HH, Weiss JC, Kwoh CK, Donohue JM, Gordon AJ, Cochran G, Malone DC, Kuza CC, Gellad WF. Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study. PLoS One. 2020 Jul 17;15(7):e0235981. doi: 10.1371/journal.pone.0235981. eCollection 2020. |
| Label | URL |
|---|---|
| Center for Pharmaceutical Policy \& Prescribing website | View source |
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| ID | Term |
|---|---|
| D000083682 | Opiate Overdose |
| D009293 | Opioid-Related Disorders |
| ID | Term |
|---|---|
| D062787 | Drug Overdose |
| D063487 | Prescription Drug Misuse |
| D000076064 | Drug Misuse |
| D019966 | Substance-Related Disorders |
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This interventional study will be a cluster randomized trial across UPMC primary care practices. Practices will be randomized into one of 3 clinician-targeted intervention groups:
The electronic health record (EHR) based intervention will be applied in the participating practices for clinicians whose patients have been identified as elevated-risk through the risk prediction algorithm.
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| EHR-Embedded Elevated-Risk Flag with Behavioral Nudges | Behavioral | Clinicians seeing patients at elevated predicted risk for opioid overdose will see a flag on the EHR storyboard indicating that the patient is at elevated predicted risk. Clinicians will also receive up to 4 best practice alerts/behavioral nudges during an in-person or telephone primary care encounter with elevated risk patients when certain requirements are met: 1) if the patient does not have an active naloxone prescription on their medication list, the clinicians will receive an active choice alert during any medication ordering to encourage naloxone prescription; 2) if the patient's opioid dosage is >50 MME, OR they are ordered a new opioid prescription, OR they have an overlapping opioid and benzodiazepine prescription order, the clinicians will receive an accountable justification alert when the relevant order is entered. |
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| Usual Care | Behavioral | Patients in the practices randomized to the Usual Care arm will receive standard care practice without change. |
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| Assessed at 6 months following study enrollment (i.e., at 6 months after the first encounter in the study period. An encounter refers to the 1st primary care visit for a patient enrolled in the study.) |
| Active Naloxone Prescription | The presence of an active naloxone prescription, determined by evidence of a naloxone order, a naloxone fill, or both. | Assessed at 4 & 6 months after study enrollment by reviewing data from 12 months preceding index date (i.e., at 4 & 6 months after enrollment). An active naloxone prescription is recorded if one exists at any point during the year before the index date. |
| Average Daily Opioid Dosage > 50 MME | The average daily opioid dosage, calculated in morphine milligram equivalents (MME), is evaluated using prescription fills data as the primary source. If fills data are unavailable, prescription orders data will be used. This measure reflects whether the average daily MME exceeds 50 during the specified time period. | Assessed at 4 and 6 months after study enrollment, based on the average daily MME calculated over the 7 days preceding the index date (i.e., 4 and 6 months after enrollment). |
| Overlapping Opioid Benzodiazepine Prescriptions | This outcome evaluates overlapping opioid and benzodiazepine use, determined by the following criteria: (A) Active overlap on the index date: Both an opioid and a benzodiazepine prescription fill are active on the index date. (B) Historical overlap: At least one opioid and one benzodiazepine prescription order within the past 28 days. Overlap is defined as meeting any of the above. | Assessed at 4 and 6 months after study enrollment, based on overlap occurring on the index date (i.e., 4 and 6 months after enrollment) or within the 28 days preceding the index date. |
| Overlapping Opioid Benzodiazepine Prescriptions Where Average Daily Opioid MME > 50 | This outcome evaluates the presence of overlapping opioid and benzodiazepine use where opioid MME is > 50, defined as above. | Assessed at 4 and 6 months after study enrollment, based on overlap occurring on the index date (i.e., 4 and 6 months after enrollment) or within the 28 days preceding the index date. |
| Emergency Department or Inpatient Visits | This outcome measures the occurrence of any emergency department (ED) or inpatient visit during the specified time period. | Assessed at 4 and 6 months after study enrollment, based on visits occurring within the 30 days prior to the index date (i.e., 4 and 6 months after enrollment). |
| Emergency Department or Inpatient Visits for Overdose | This outcome measures the occurrence of any emergency department (ED) or inpatient visit specifically attributed to an overdose during the specified time period. | Assessed at 4 and 6 months after study enrollment, based on visits occurring within the 30 days prior to the index date (i.e., 4 and 6 months after enrollment). |
| 33735222 | Background | Lo-Ciganic WH, Donohue JM, Hulsey EG, Barnes S, Li Y, Kuza CC, Yang Q, Buchanich J, Huang JL, Mair C, Wilson DL, Gellad WF. Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach. PLoS One. 2021 Mar 18;16(3):e0248360. doi: 10.1371/journal.pone.0248360. eCollection 2021. |
| 35623798 | Background | Lo-Ciganic WH, Donohue JM, Yang Q, Huang JL, Chang CY, Weiss JC, Guo J, Zhang HH, Cochran G, Gordon AJ, Malone DC, Kwoh CK, Wilson DL, Kuza CC, Gellad WF. Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study. Lancet Digit Health. 2022 Jun;4(6):e455-e465. doi: 10.1016/S2589-7500(22)00062-0. |
| 35315173 | Background | Guo J, Gellad WF, Yang Q, Weiss JC, Donohue JM, Cochran G, Gordon AJ, Malone DC, Kwoh CK, Kuza CC, Wilson DL, Lo-Ciganic WH. Changes in predicted opioid overdose risk over time in a state Medicaid program: a group-based trajectory modeling analysis. Addiction. 2022 Aug;117(8):2254-2263. doi: 10.1111/add.15878. Epub 2022 Apr 3. |
| Background | Hulsey E, Hershey TB, Parker LS, Kuza C, Fedro-Byrom S, Gellad WF. Overdose Risk Prediction Algorithms: The Need for a Comprehensive Legal Framework. Health Affairs Forefront. 2022 November 22. doi: 10.1377/forefront.20221118.549875. |
| 37001323 | Background | Gellad WF, Yang Q, Adamson KM, Kuza CC, Buchanich JM, Bolton AL, Murzynski SM, Goetz CT, Washington T, Lann MF, Chang CH, Suda KJ, Tang L. Development and validation of an overdose risk prediction tool using prescription drug monitoring program data. Drug Alcohol Depend. 2023 May 1;246:109856. doi: 10.1016/j.drugalcdep.2023.109856. Epub 2023 Mar 27. |
| 39420438 | Background | Nguyen K, Wilson DL, Diiulio J, Hall B, Militello L, Gellad WF, Harle CA, Lewis M, Schmidt S, Rosenberg EI, Nelson D, He X, Wu Y, Bian J, Staras SAS, Gordon AJ, Cochran J, Kuza C, Yang S, Lo-Ciganic W. Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings. Bioelectron Med. 2024 Oct 18;10(1):24. doi: 10.1186/s42234-024-00156-3. |
| 39569464 | Background | Militello LG, Diiulio J, Wilson DL, Nguyen KA, Harle CA, Gellad W, Lo-Ciganic WH. Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support. J Am Med Inform Assoc. 2025 Feb 1;32(2):398-403. doi: 10.1093/jamia/ocae291. |
| 42081274 | Derived | Gellad WF, Chen YF, Park TW, Yang Q, Arnold JD, Kuza CC, Fedro-Byrom SN, Diiulio J, Militello LG, Whitlock M, Sadhu EM, Visweswaran S, Fine MJ, Abebe KZ, Suda KJ, Lo-Ciganic WH. Machine Learning Prediction and Reducing Overdoses With Electronic Health Record Nudges (mPROVEN) in the Primary Care Setting: Protocol for a Cluster Randomized Controlled Trial. JMIR Res Protoc. 2026 May 4;15:e94007. doi: 10.2196/94007. |
| D064419 |
| Chemically-Induced Disorders |
| D000079524 | Narcotic-Related Disorders |
| D001523 | Mental Disorders |